Bayesian coarsening: rapid tuning of polymer model parameters

نویسندگان

چکیده

Abstract A protocol based on Bayesian optimization is demonstrated for determining model parameters in a coarse-grained polymer simulation. This process takes as input the microscopic distribution functions and temperature-dependent density targeted system. The then iteratively considers simulations to sample space of parameters, aiming minimize discrepancy between new target. Successive samples are chosen using optimization. Such can be employed systematically expensive high-resolution extend accessible length time scales make contact with rheological experiments. coarsening compared previous machine-learned parameterization technique which required high volume training data. found precisely efficiently discover appropriate spite rough noisy fitness landscapes, due natural balance exploration exploitation

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ژورنال

عنوان ژورنال: Rheologica Acta

سال: 2023

ISSN: ['0035-4511', '1435-1528']

DOI: https://doi.org/10.1007/s00397-023-01397-w